KDD-93: Progress and Challenges in Knowledge Discovery in Databases

Shapiro 1992) devoted or closely related to discovery in databases. The application side is of interest to any business or organization with large databases. KDD applications have been reported in many areas of business, government, and science (Parsaye and Chignell 1993; Inmon and Osterfelt 1991; Piatetsky-Shapiro and Frawley 1991). The notion of discovery in databases has been given various names, including knowledge extraction, data mining, database explo■ Over 60 researchers from 10 countries took part in the Third Knowledge Discovery in Databases (KDD) Workshop, held during the Eleventh National Conference on Artificial Intelligence in Washington, D.C. A major trend evident at the workshop was the transition to applications in the core KDD area of discovery of relatively simple patterns in relational databases; the most successful applications are appearing in the areas of greatest need, where the databases are so large that manual analysis is impossible. Progress has been facilitated by the availability of commercial KDD tools for both generic discovery and domain-specific applications such as marketing. At the same time, progress has been slowed by problems such as lack of statistical rigor, overabundance of patterns, and poor integration. Besides applications, the main themes of this workshop were (1) the discovery of dependencies and models and (2) integrated and interactive KDD systems.